Baselines: We use two baselines for VisDial v1.0: 1) FGA [1]}, an attention unit inspired from graphical models that infers an attention map for each modality; and 2) LS [2]}, which pre-trains on related vision-language datasets, e.g., Conceptual Captions and Visual Question Answering [3]}, [4]}. We also report LS (CE), which finetunes on the dense annotations, at the expense of MRR performance.
